Diagnosis of many medical conditions requires the collection and analysis of medical data. In interpreting this data, doctors and other medical personnel have generally applied a number of rules of thumb, or qualitative assessments, to reach their diagnosis. These rules of thumb have proven to be quite useful but are not comprehensive, because certain ailments and abnormalities cannot be adequately identified merely by applying currently established rules of thumb.
One example where rules of thumb are applied is in monitoring electrocardiograph (EKG) data. EKG data is typically presented as a graphical output of a patient's heart activity. Doctors look for recognizable abnormalities and particular flags in the EKG data, as warning signals of health problems. They can discern certain abnormalities amongst this data by visually inspecting the graphical output; however, other important, more subtle abnormalities may go undetected. As such, the visual examination of data does not provide a complete diagnostic tool because some potentially significant abnormalities in the data are not apparent from visual inspection.
Another example of where rules of thumb are applied is in monitoring hormone secretion in an attempt to identify abnormal physiology. In the past fifteen years, endocrinologists have determined that episodic hormone secretion is a widespread phenomenon. The discovery of the link between abnormal pulsatility and certain hormonal disorders has prompted the recognition that a greater understanding of hormone secretion patterns, statistic to analyze hormone secretion data, and underlying system models could be of keen importance. To date, a number of pulse-identification algorithms have been developed to analyze hormone level data. These methods have been useful in detecting abnormal secretory patterns in some instances, and the expectation is that refined versions of these algorithms, applied to increasingly accurate and numerous data, will detect further abnormalities in hormonal secretion, earlier in the course of disease.
Another rule of thumb is used in fluid dynamics to design structures. Through experimentation, a force ratio between the inertial force and the viscous force of fluids has been developed. This ratio, or Reynolds number, is correlated with the formation of wakes when a fluid flows past an object. In systems with a fluid flowing at a fixed velocity and impinging on a rigid object, the wake behavior can be modelled. The Reynolds number cannot be easily used to model more complex systems.
For example, the Reynolds number cannot easily model a human heart because blood flow is not constant and the heart is not a rigid structure. The blood changes the heart surface dynamically and nonlinearly. Designers of artificial hearts rely heavily on trial and error, with the testing often being fatal. Artificial heart valves change the pattern of fluid flow in the heart, which creates areas of turbulence and areas of stagnation. Blood clots that form in the stagnation areas often find their way to the patient's brain, causing strokes.